The AI coding trap
4 hours ago
- #AI-coding
- #tech-leadership
- #software-development
- Software development is primarily about problem-solving, with coding being just a small part of the process.
- AI-driven coding speeds up writing code but lacks context, leading to more time spent understanding and fixing AI-generated code.
- The difference between marketing claims of AI coding speed (10X faster) and actual productivity gains (around 10%) is significant.
- Developers spend more time fixing AI output, handling tasks like testing, documentation, and deployment, rather than coding.
- The 'tech lead’s dilemma' involves balancing delegation for team growth versus taking on hard tasks for faster delivery.
- Siloing expertise in the tech lead leads to team brittleness, burnout, and crisis when they leave.
- Effective technical leadership involves balancing delivery with team growth through practices like code reviews, TDD, and pair programming.
- AI coding agents are like lightning-fast junior engineers but lack true learning capacity.
- Two approaches to AI coding: 'AI-driven engineering' (sustainable) vs. 'vibe coding' (fast but messy).
- To avoid the AI coding trap, engineers must set practices for AI agents, integrating them into all stages of the software lifecycle.